Search Results - "fine-tuning"

Modulation recognition technology, as one of the core technologies in the field of wireless communications, holds significant importance in intelligent communication systems such as link adaptation and IoT devices. In recent years, deep learning-based automatic modulation recognition (DL-AMR) has em...

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The growing volume of unstructured text data in the banking sector has created a need for advanced classification methods to manage customer inquiries efficiently, resulting in faster response times, automated message classification, and reduced human errors. The classification results are integrate...

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Citra generasi AI memiliki beragam manfaat yang signifikan, baik dalam bidang penelitian maupun industri. Namun, penggunaan citra generasi AI juga memiliki dampak negatif dalam konteks hukum, politik dan berbagai aspek lain dalam kehidupan. Penelitian ini menitik beratkan klasifikasi citra generasi...

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BACKGROUND: Deep learning’s role in blood film screening is expanding, with recent advancements including algorithms for the automated detection of sickle cell anemia, malaria, and leukemia using smartphone images. OBJECTIVES: This study aims to build the artificial intelligence (AI) models and asse...

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Glaucoma is an irreversible neurodegenerative disease that affects the optic nerve, leading to partial or complete vision loss. Early and accurate detection is crucial to prevent vision impairment, which necessitates the development of highly precise diagnostic tools. Deep learning (DL) has emerged...

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Deep learning approaches for pork freshness grading typically require large datasets, which limits their practical application due to the high costs associated with data collection. To address this challenge, we propose BBSNet, a lightweight few-shot learning model designed for accurate freshness cl...

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Large Language Models (LLMs) have shown remarkable capabilities in various applications, including robotics, telecommunications, and scientific discovery. While much attention has been given to LLM inference and training phases, fine-tuning has received less focus despite its increasing cost, especi...

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Aquaculture has emerged as an important pillar of global food production, and shrimp farming plays a critical role in fulfilling the growing demand for seafood. This is especially true in Ecuador, which is recognized as one of the world's largest exporters and producers of shrimp. However, conv...

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The burgeoning sophistication of Artificial Intelligence (AI) has catalyzed the rapid proliferation of Large Language Models (LLMs) within software development. These models are increasingly employed to automate the generation of functionally correct code, address complex computational problems, and...

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Advanced Air Mobility (AAM) aims to integrate unmanned aerial vehicles (UAVs) into urban airspace for efficient cargo and passenger transport, relying on autonomous navigation within designated 3D air corridors. Deep reinforcement learning (DRL) has demonstrated significant potential for autonomous...

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Early detection of morbidities that complicate pregnancy improves health outcomes in low- and middle-income countries. Automatic revision of electronic health records (EHRs) can help identify such morbidity risks. There is a lack of corpora to train models in Spanish in specific domains, and there a...

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Tea is widely regarded as one of the most popular beverages globally, and Bangladesh plays a significant role both as a producer and consumer of this renowned drink. However, diseases that impact the quality and productivity of crops can greatly impede the production of tea, impacting the final prod...

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The structure and electronic properties of nitrides have garnered significant research interest. To improve prediction efficiency and generalization ability, this study proposes an large language model (LLM) fine-tuning method trained by data with description of nitride crystal structure, obtaining...

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Large language models (LLMs) are trained on huge datasets, which allow them to answer questions from various domains. However, their expertise is confined to the data that they were trained on. In order to specialize LLMs in niche domains like healthcare, various training methods can be employed. Tw...

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Large language models (LLMs) are revolutionizing specialized fields by enabling advanced reasoning and data synthesis. Environmental science, however, poses unique hurdles due to its interdisciplinary scope, specialized jargon, and heterogeneous data from climate dynamics to ecosystem management. De...

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Poor urban air quality is a major public health concern, especially in highly urbanized areas. This study aims to predict health risks associated with air pollution using machine learning techniques based on environmental variables. The dataset used, Urban Air Quality and Health Impact, contains 1,0...

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Along with the rapid technological progress in the field of artificial intelligence, music generation algorithms based on large-scale pre-trained models have increasingly become the focus of academic attention. Existing polyphonic music generation techniques have limitations in terms of melodic comp...

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<b>Background:</b> Brain tumors are among the most difficult diseases to deal with in modern medicine due to the uncontrolled cell proliferation, which causes grave damage to the nervous system. Brain tumors can be broadly classified into two categories: primary tumors, which originate w...

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Accurate electric vehicle (EV) load forecasting is crucial for efficient grid operations and demand-side management, yet it is challenging in data-scarce scenarios. Transfer learning (TL) offers a solution by transferring knowledge from data-rich to data-limited scenarios. However, when the knowledg...

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Cybersecurity is a growing area of research due to the constantly emerging new types of cyberthreats. Tools and techniques exist to keep systems secure against certain known types of cyberattacks, but are insufficient for others that have recently appeared. Therefore, research is needed to design ne...

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